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Produktbild: Trapping of Small Organisms Moving Randomly

Trapping of Small Organisms Moving Randomly Principles and Applications to Pest Monitoring and Management

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Erscheinungsdatum

31.03.2015

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Springer

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114 (Printausgabe)

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7489 KB

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Englisch

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9783319129945

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Erscheinungsdatum

31.03.2015

Verlag

Springer

Seitenzahl

114 (Printausgabe)

Dateigröße

7489 KB

Sprache

Englisch

EAN

9783319129945

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  • Produktbild: Trapping of Small Organisms Moving Randomly
  • TABLE OF CONTENTS Chapter 1. Why Care about Small Animals Moving Randomly This chapter documents the acute need for quick and inexpensive estimation of absolute density of small animals. It explains what alternative tools are available for this purpose and why they are impractical for everyday pest management decisions. Chapter 2. Trap Function and Overview of the Trapping ProcessThis section defines a trap and articulates the various roles traps play, including increasing the efficiency whereby animals are detected, harvested, and thwarted from being pests (mass trapping).This section explains that trapping is a process of intersection of moving targets with the trap or with an attractant plume emanating from the trap. Diagrams are offered for typical spatial contexts of trapping. Six steps of trapping are articulated that fall into three main categories: finding the trap (findability), engaging the trapping mechanism (efficiency), and keeping the catch contained in the trap (retention). The concept of random walks is introduced and explanation is provided for why the composite probability for findability, efficiency, and retention is typically very low for random movers. Definitions and examples are offered for the core elements of trapping: plume reach, maximum mover dispersion, and trapping radius and area. A foundational trapping equation is offered that postulates catch is the sum across a trapping area of probability of catch when movers originate at a particular distance from a trap multiplied by the number of movers originating at that given distance.  The key parameters of trapping are identified as a setup for detailed analysis in the following three chapters. Chapter 3. Random Displacement in the Absence of CuesThis section summarizes key features of random walks and details the computer software that author Paul Weston wrote for simulating random walks with manipulable step lengths, meander (amount of turning), and run time.  Diagrams are presented for classical random walks of which diffusing molecules and Brownian motion are exemplars.This section contrasts tracks of molecules (very high local meander) with those for displacing organisms (low local meander) and explains how the distribution of turn angles comprising a stretch of track can be plotted so as to reveal the spread (circular standard deviation, or c.s.d.) of the normal distribution that results when new headings are selected randomly. Chapter 4. The Geometry of Trap InterceptionsThis section demonstrates that the proportion of straight-line movers that will be intercepted by a trap upon departing on random headings from a common origin is trap length divided by the circumference of a circle whose radius is the distance from mover origin to the trap. Thus, the relationship between proportion of interceptions and distance from a trap is an inverse function, not a log function as other trapping researchers have just assumed. We then document that the graphical technique of plotting distance of origin from a trap on the x-axis against 1/catch on the y-axis generates a straight line whose slope can be used to calculate trap length or plume reach. This section establishes that the relationships developed above for trap interceptions of ballistic movers hold also for random walkers. However, when the slope of an inverse plot of release distance vs. 1/proportion caught is used to mathematically extract plume reach, an additional length is registered that we call "gain."  Gain results from functional track broadening due to meander. Gain is a positive attribute of foraging animals because it increases the efficiency of resource finding during local search. Chapter 5. Interpreting Catch in the Single TrapHere we establish that catch in a trap is equal to the proportion of all animals in a trapping area that get caught (designated Tfer) multiplied by the number of animals in that trapping area (designated Mden). This section demonstrates how a data set on the proportion of animals caught from a specified distance from a trap (spTfer) can be correctly converted into mean overall proportion of movers from the whole trapping area that get caught (Tfer). Then we show how absolute animal density can be calculated using the simple trapping equation once catch number and Tfer are known.  Examples are given from simulations and real-world field experiments with insects that validate this simple trapping equation.Chapter 6. Competing TrapsWe define traps as competing when the presence of one or more traps reduces catch in a given trap below what would have been measured if the additional trap or traps were not present.Complete competition occurs when traps are so closely deployed that they split arriving animals equally. Then the familiar mathematics of playing card probabilities apply. Chapter 7. Proposed Experimental Method for Measuring C.S.D. of Random Walkers Via a Trapping-Grid Our research with grids of traps and patterns of catch across them suggested that the c.s.d. employed by foraging animals might be revealed through such analysis. We go on to explore the interactions of simulated movers with a regularly spaced 5 x 5 grid of traps. Indeed, a useful pattern of catch was generated when a population of movers was released at a point just outside of and nearby a corner trap. Catch fell with the negative exponential of distance from the corner. These negative exponents were found by simulations to be tightly linearly correlated with mover c.s.d. Thus, a standard curve was produced that should prove useful in translating field catches of real animals into c.s.d. estimates, something that was entirely out of reach until now. This section gives step-by-step instruction on how the above method for c.s.d. estimation can be conducted in the field with animals like insects. Field experiments testing this procedure for moth pests of fruit are currently underway.Chapter 8. Trapping to Achieve Pest Control Directly This section defines the purpose of mass trapping and its vocabulary.Here we explain why efficacy of behavioral control tactics is pest-density dependent even though suppression of catch in a monitoring trap (the standard measure of effect for behavioral controls) is pest-density independent. This knowledge resolves long-standing confusion about why % catch suppression is not well correlated with realized crop protection.Chapter 9. Automated Systems for Recording, Reporting, and Analyzing Trapping Data This chapter opens with the observation that profit margins in agriculture are so thin that the labor required to deploy and service standard pest monitoring traps is becoming prohibitive. We argue that automation of monitoring systems is appropriate and inevitable in the face of rapidly evolving and ever-cheaper information technologies.This section briefly traces the evolution of automated traps beginning with mechanical devices shifting the collection vessel so that the timing of catch was revealed. A leap occurred in the evolution trap automation when in the mid-1980s D. E. Hendricks used an infrared sensor to electronically record the arrivals of moths in a pheromone-baited trap. He then adopted existing technology to wirelessly transmit the data to a central station for analysis. Others have advanced automated trapping to where the records of insect arrivals are highly accurate and the data are being immediately and accurately transmitted by adaptations of the ever-improving and now ubiquitous cell-phone system at affordable pricing. References Cited:Bibliography (offers a chronology of publications with key concepts of trapping)